AQEA: Domain-Adaptive Semantic Compression of Embeddings — Achieving Extreme Ratios with High Semantic Preservation
Embedding models power modern AI applications, but their high dimensionality poses challenges for storage, RAM, and inference speed in large-scale retrieval. Traditional compression methods (e.g., PQ, scalar quantization) often sacrifice semantic structure.
AQEA (Aurora Quantum Encoding Algorithm) introduces a patent-pending, domain-adaptive approach to semantic compression, achieving 300–585× ratios while preserving >94% Spearman correlation and retrieval performance across modalities (text, video, audio, proteins).
Core Innovation
AQEA uses algebraic compression with steerable "semantic lenses" — small trainable weights (~35 KB) that adapt retrieval focus (discovery, balanced, precision, or custom) without retraining the base model.
Key features:
- Multimodal & domain-adaptive: Trains on small datasets, generalizes to unseen data.
- Extreme efficiency: Up to 99% storage/RAM savings, compatible with Pinecone, Weaviate, etc.
- High preservation: 97% on video similarity, 94–95% on other tasks.
Benchmarks & Reproducibility
Public datasets, hashes, and CLI/API tool (free tier: 10k compressions/month) ensure full reproducibility. Custom lens training in minutes on CPU.
Applications & Impact
- Video/multimodal search at scale.
- Biotech (protein embeddings).
- E-commerce/RAG systems with millions of vectors.
The method outperforms traditional quantization in semantic retention while enabling new deployment scenarios.
Resources
- Full Technical Report: https://zenodo.org/records/18138436 (DOI: 10.5281/zenodo.18138436)
- Demo & Tool: [Link zu eurer Platform, falls verfügbar]
- Related: Ground-Truth-Aware Evaluation Proposal
AQEA pushes the boundaries of efficient AI infrastructure. Try the free tier and share your results — what compression ratios do you need in your projects?
#Embeddings #SemanticCompression #MachineLearning #VectorDB #RAG #AIInfrastructure